Dr. Nasima Arshad | Smart Materials | Women Researcher Award

Allama Iqbal Open University | Islamabad | Pakistan

Dr. Nasima Arshad is an accomplished academic and researcher in the field of smart materials and physical chemistry, currently serving as an Associate Professor at Allama Iqbal Open University, Islamabad. With extensive experience in teaching, research, and academic development, she has played a pivotal role in shaping chemistry education through curriculum design, course coordination, and the establishment of advanced laboratory facilities. Her research expertise spans electrochemistry, spectroscopy, material synthesis, and smart functional materials, with particular emphasis on hydrogels, nanocomposites, energy storage systems, and biomedical applications. She has supervised a significant number of graduate and postgraduate research projects, contributing to the development of future scientists and researchers. Dr. Arshad has authored a substantial body of scholarly work, including numerous peer-reviewed publications, book chapters, and a specialized academic book, reflecting her strong commitment to scientific advancement. Her work often integrates material science with real-world challenges such as energy sustainability, environmental protection, and healthcare innovation. In addition to her research contributions, she has actively participated in and organized national and international conferences, workshops, and training programs. Her dedication to academic excellence, interdisciplinary collaboration, and impactful research has established her as a respected figure in the field of smart materials and chemical sciences.

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Assoc. Prof. Dr. Ming Shao | Smart Materials | Research Excellence Award

Beijing Forestry University | China

Assoc. Prof. Dr. Ming Shao is a distinguished researcher in smart materials and intelligent environmental systems, with a strong interdisciplinary background that integrates materials science concepts with urban computing, ecological modeling, and sustainable spatial design. He serves as an Associate Professor at the School of Landscape Architecture, Beijing Forestry University, where his research advances data-driven and smart approaches to understanding and optimizing complex urban material–environment systems. Dr. Shao’s work focuses on the interaction between smart materials concepts, green infrastructure, and ecosystem services, particularly in high-density urban environments where spatial efficiency and environmental performance are critical. His research addresses urban green space optimization, biodiversity enhancement, carbon sequestration, and ecosystem service assessment using advanced computational methods, spatial modeling, and intelligent simulation techniques. By applying system dynamics, urban computing, and multi-scale spatial analysis, he contributes to the development of resilient and adaptive urban environments that respond intelligently to ecological and societal demands. Dr. Shao has led and participated in numerous nationally competitive research projects, demonstrating strong leadership in interdisciplinary research that connects smart material behavior, environmental performance, and urban sustainability. His work supports evidence-based planning strategies that enhance ecological functionality, climate resilience, and human well-being in rapidly urbanizing regions. In addition to his research activities, he actively contributes to academic service through peer review for leading international journals and engagement in professional scientific communities. Dr. Shao is also committed to education and mentorship, guiding students in innovative research that bridges smart materials, ecological systems, and digital technologies. Through his interdisciplinary vision, methodological innovation, and sustained contributions to intelligent and sustainable material–environment systems, Assoc. Prof. Dr. Ming Shao has established a strong scientific profile and is a highly deserving recipient of the Research Excellence Award.

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Dr. Wentao Zhou | Smart Materials | Research Excellence Award

The College of Intelligent Systems Science and Engineering | Harbin Engineering University | China

Dr. Wentao Zhou is an emerging researcher in smart materials and intelligent systems, recognized for his growing academic impact and innovative contributions to advanced material technologies. He has developed a strong research portfolio with an h-index of 4, supported by 9 published documents and 56 citations across 51 citing documents, reflecting the influence and relevance of his scientific work. Dr. Zhou is affiliated with the College of Intelligent Systems Science and Engineering at Harbin Engineering University, where he has built a multidisciplinary background spanning deep learning, computer vision, and small-object detection with applications in material characterization and intelligent sensing. His research excellence is further demonstrated through the publication of 10 peer-reviewed SCI papers, multiple competition achievements, and significant innovation output, including 3 authorized Chinese patents and several ongoing patent activities. He also contributes to technological development as a key technical backbone in collaborative projects, independently leading planning, algorithm design, personnel coordination, and the establishment of monitoring, identification, and testing standards for air-traffic-control systems. Dr. Zhou’s work is strengthened by academic exposure at globally ranked institutions and active professional engagement as a Graduate Student Member of IEEE. He has also earned more than 20 prestigious honors and scholarships, recognizing both academic excellence and technological innovation. Beyond his research achievements, he has held leadership roles such as Workshop Chair for RAITS, reflecting his commitment to academic service and community contribution. His core research in smart materials integrates intelligent algorithms with material-focused applications, positioning him as a promising young scientist whose innovations align strongly with the objectives of the Research Excellence Award. Dr. Zhou’s scholarly record, technological creativity, and dedication to advancing smart materials collectively underscore his merit as a dynamic and impactful researcher.

Profiles: Scopus | Orcid

Featured Publications

Yang, S., Zhou, W., Qu, S., & Khoo, B. C. (2025, December). Fast and high-accuracy state estimator for some unknown dynamic objects with a stereo camera in aerial tracking.

Wang, R., Qiao, R., Zhou, W., & Cai, C. (2025, November). HACRNet: Hierarchical attention compression for high-speed fine-grained ship recognition.

Zhou, W., Cai, C., Srigrarom, S., & Li, C. (2025, June). SAD-YOLO: A small object detector for airport optical sensors based on improved YOLOv8.

Zhang, Y., Zhao, E., Liang, H., & Zhou, W. (2024, December). MATD3 with multiple heterogeneous sub-networks for multi-agent encirclement-combat task.

Zhou, W., Cai, C., Wu, K., & Gao, B. (2024, June). LAS-YOLO: A lightweight detection method based on YOLOv7 for small objects in airport surveillance.

Mr. Fawad Khan | Smart Materials | Best Researcher Award

Shenzhen Institutes of Advanced Technology | Chinese Academy of Sciences | China

Mr. Fawad Khan is a promising researcher at the intersection of smart materials, safe robotics, reinforcement learning, and human–robot collaboration, recognized for his innovative contributions to the development of intelligent, safety-aware autonomous systems. As a PhD Researcher at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, he focuses on advancing constrained reinforcement learning frameworks that enable robots to operate reliably and safely alongside humans in dynamic and safety-critical environments such as collaborative manufacturing, warehouse automation, and assistive robotics. His scholarly output includes 3 research documents, and he has contributed to journal articles, conference papers, and manuscripts addressing precision grasping, adaptive safety constraints, multi-object manipulation, and safety-critical coordination. His research introduces novel approaches that combine tactile–visual perception, adaptive constraint satisfaction, and multi-modal learning to significantly reduce safety violations while maintaining high task performance in robotic systems. He has designed benchmark platforms for safe robotic manipulation and expanded the capabilities of existing tools such as Safety Gym to enable high-fidelity evaluation of robotic arms with multiple degrees of freedom. Prior to his doctoral research, Mr. Khan gained industry experience as a Python developer and data analyst, where he automated logistics operations, designed data-driven decision-support tools, and streamlined complex workflows, demonstrating his ability to integrate practical engineering solutions with theoretical AI advancements. His technical expertise spans reinforcement learning algorithms, constrained optimization, robotics simulation environments, computer vision, multi-modal neural networks, and high-performance computing frameworks. He actively collaborates with interdisciplinary teams working on intelligent manufacturing, safe autonomy, and human-centered robotics. With strong analytical skills, a clear research vision, and a growing academic footprint, Mr. Fawad Khan represents a new generation of AI and robotics researchers dedicated to creating safer, smarter, and more adaptive robotic systems, making him a highly deserving candidate for the Best Researcher Award.

Profile: Scopus

Featured Publications

Khan, F., Feng, W., Wang, Z., Huang, T., Xiao, L., et al. (2025). Safe reinforcement learning for objects manipulation in safety-critical coordinated tasks. In ISARC: Proceedings of the International Symposium on Automation and Robotics in Construction (Vol. 42, pp. 334–341). IAARC Publications.

Khan, F., et al. Reinforcement learning for precision grasping and safety-critical coordination in a robotic arm. Journal of Intelligent Service Robotics.

Khan, F., et al. Safe reinforcement learning for vision-based robotic manipulation in human-centered environment. Journal of Intelligent Robotics and Applications.

Khan, F., et al. Safe reinforcement learning for multi-object robotic manipulation with adaptive safety constraint. Expert Systems with Applications.

Mr. Angelos Athanasiadis | Smart Materials | Research Excellence Award

Aristotle University of Thessaloniki | AUTH | Greece

Mr. Angelos Athanasiadis is an emerging researcher in smart materials, embedded intelligence, and high-performance computing architectures, known for his contributions to FPGA-accelerated deep learning and intelligent cyber-physical systems. Currently pursuing his PhD in Electrical and Computer Engineering at Aristotle University of Thessaloniki, he focuses on designing advanced hardware-accelerated frameworks that significantly enhance the speed, efficiency, and energy performance of full-precision Convolutional Neural Networks on modern AMD FPGA platforms. His early scientific influence is reflected in 5 citations, referenced across 4 citing documents, supported by ongoing scholarly outputs and an h-index recorded as 1–2, demonstrating his growing visibility in computational engineering research. Mr. Athanasiadis has contributed to significant EU-funded research initiatives, including the ADVISER and REDESIGN projects, where he developed high-fidelity emulation tools, hardware–software co-design solutions, and distributed embedded intelligence for heterogeneous systems combining CPUs, GPUs, and FPGAs. His work on FUSION an open-source, timing-accurate, multi-node emulation framework integrating QEMU with OMNeT++ using HLA/CERTI synchronization has advanced the ability to accurately prototype next-generation smart systems for robotics, aerial monitoring, real-time analytics, and autonomous decision-making. With a strong background in electronics, embedded systems, and management engineering, he has also completed industry-driven research roles at EXAPSYS, SEEMS PC, and Cadence Design Systems, contributing to R&D for sensor networks, FPGA-based acceleration pipelines, and complex digital-system workflows. In addition to his technical expertise, he maintains interdisciplinary strengths in AI-driven system optimization, hardware–software integration, multiphysics emulation, and intelligent system design. Collaborating with leading academic researchers and contributing to peer-reviewed venues, he continues to expand his research footprint. With strong analytical skills, innovation-oriented thinking, and a commitment to advancing smart materials and high-performance embedded intelligence, Mr. Angelos Athanasiadis stands out as a promising researcher and a deserving candidate for the Research Excellence Award.

Profiles: Google Scholar | Orcid

Featured Publications

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2025). Energy-efficient FPGA framework for non-quantized convolutional neural networks. arXiv Preprint, arXiv:2510.13362.

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2025). An efficient open-source design and implementation framework for non-quantized CNNs on FPGAs. Integration, Article 102625.

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2024). An open-source HLS fully parameterizable matrix multiplication library for AMD FPGAs. WiPiEC Journal—Works in Progress in Embedded Computing Journal, 10(2).

Katselas, L., Jiao, H., Athanasiadis, A., Papameletis, C., Hatzopoulos, A., … (2017). Embedded toggle generator to control the switching activity during test of digital 2D-SoCs and 3D-SICs.

Katselas, L., Athanasiadis, A., Hatzopoulos, A., Jiao, H., Papameletis, C., … (2017). Embedded toggle generator to control the switching activity.